Ultra low power platform for remote health monitoring

ABSTRACT

An apparatus and method is described to sense sparse signals from a medical device using compressed sensing and then transmitting the data for processing in the cloud.

PRIORITY CLAIM

This application claims the benefit under 35 USC 119(e) to U.S.Provisional Patent Application Ser. No. 61/852,967, filed on Mar. 26,2013 and titled “A compressed sensor platform for remote healthmonitoring,” which is incorporated by reference herein.

TECHNICAL FIELD

The present invention relates to the field of sensor implementations. Inparticular, an apparatus and method is described to sense sparse signalsfrom a medical device using compressed sensing and then transmitting thedata for processing in the cloud.

BACKGROUND OF THE INVENTION

A typical prior art sensor network used for remote health monitoring isdepicted in FIG. 1. The two main components are sensor block 200 andcomputing device 300, which here is an intelligent backend device.Sensor block 200 typically is a medical device for obtaining data from apatient, such as an electroencephalograph, cardiotocograph, or otherdevice. Sensor block 200 comprises an amplifier (100), ananalog-to-digital (ADC) conversion block 101, a post processor 102, atransmitter 103, and an antenna 104. It is to be understood that othersensor blocks similar to sensor block 200 can be used with the samecomputing device 300. For brevity's sake, only sensor block 200 isdepicted in FIG. 1.

Computing device 300 may be a PC, server or any product with processingcapabilities. Sensor block 200 obtains data from a patient, such asbrain signals, heart signals, temperature, etc. using electrodes orother means, amplifies the sensed analog signals using amplifier 100,converts the analog signal into digital data using analog-to-digitalconversion block 101, and processes the raw digital data using postprocessor 102, which can packetize the data, add headers, encrypt thedata, and perform other known techniques. The packetized data is thensend to computing device using transmitter 103 and antenna 104 overnetwork 105. Network 105 can be a wireless network, a hardwired network,or a combination of the two.

The prior art sensor network of FIG. 1 has several drawbacks. First,sensor block 200 consumes a substantial amount of power. This is mainlybecause the sensor runs at full speed. As an example, if sensor block200 is generating electroencephalography (EEG) signals, the signalbandwidth will include frequencies up to 1 KHz, and analog-to-digitalconverter 20 will need to perform sampling of the analog signal at arate of at least 2 KHz (which is the Nyquist rate of the highestfrequency in the signal). In addition, transmitter 103 will needs totransmit at that same rate, 2 KHz. In a typical medical device,transmitter 103 can consume 80% of the total power consumed by thedevice. For some applications where packetization and encryption needsare large, the post processing block may be the power bottleneck sinceit too runs at or above the Nyquist rate.

Second, computing device 300 needs to store all the data it receives andprocess it. Typically the computing device 300 will process the receiveddata and take actions in response to the data (for example, begin anaudio alarm). It can be appreciated that computing device 300 performs asubstantial amount of data analysis and typically will generate a userinterface that creates a visual display of the data obtained by sensorblock 200. The large amount of data leads to high storage costs andconsumes a significant amount of processing time and power.

Third, security is a major implementation drain. Sending data overwireless links requires some mode of encryption, all of which requireextra power and resources.

What is needed is an improved sensor network that with sensor blocksthat transmit less data and a computing device that operates on lessdata than in prior art sensor networks.

SUMMARY OF THE INVENTION

The aforementioned problem and needs are addressed through an embodimentthat utilized compressed sensing within the sensor block. Compressedsensing can be used to process analog signals that are sparse in nature,meaning that the signal is periodic and does not change significantlyover time. The human body naturally generates many signals that aresparse in nature, such as heart beat, brainwaves, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a prior art sensor network.

FIG. 2 depicts an embodiment of a sensor network utilizing compressedsensing and a cloud.

FIG. 3 depicts an embodiment of a compressed sensing analog-to-digitalconversion block.

FIG. 4 a depicts the prior art Nyquist method.

FIG. 4 b depicts the prior art compressed sensing method.

FIG. 5 depicts an embodiment of a sensor network utilizing compressedsensing used with a Body Area Network.

FIG. 6 depicts an embodiment of a sensor network utilizing compressedsensing used with a Body Area Network.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

To solve the issues outlined in the prior art section, a new platformsensor network is shown in FIG. 2. Sensor block 210 shares certainblocks with prior art sensor block 200. Namely, sensor block 210comprises amplifier 100, post processor 102, transmitter 103, andantenna 104. However, unlike the prior art sensor block 200, sensorblock 210 contains compressed sensing analog-to-digital conversion block150. It is to be understood that other sensor blocks similar to sensorblock 210 can be used with the same computing device 300. For brevity'ssake, only sensor block 210 is depicted in FIG. 2. Compressed sensing isa known technique in other fields. A seminal paper on compressed sensingis “An Introduction to Compressive Sampling,” Emmanuel J. Candes andMichael B. Wakin, IEEE Signal Processing Magazine, March 2008, which isincorporated by reference herein.

Sensor block 210 communicates with computing device 300 over network115. Computing device 300 can communicate with the cloud 400 overnetwork 120. Network 115 and network 120 each can comprise a wireless orhardwired network or a combination of the two. Network 115 preferably isa cellular network, such as a 3G or 4G network, and transmitter 103 iscapable of transmitting signals over such a network.

In one embodiment, amplifier 100 is implemented as a switched capacitoramplifier with a chopper or other means of mitigating 1/f noiseAmplifier 100 optionally can be coupled to one or more electrodes usedto measure electrical data directly from a human patient. Compressedsensing analog-to-digital conversion block 150 can be implemented usinga switched capacitor multiplier. Post processor 102 can be implementedas a programmable state machine which can be configurable for variousstandards and security requirements. Transmitter 103 can be an ultra lowpower switched capacitor class C amplifier. In this way the whole systemcan be implemented with a CMOS ASIC and a few external components suchas antenna and bio-medical tissue interface.

Additional detail regarding compressed sensing analog-to-digitalconversion block 150 is shown in FIG. 3 and FIGS. 4 a and 4 b. FIG. 4 adepicts the traditional Nyquist sampling method, and FIG. 4 b depictsthe more recently developed compressed sensing method. By not having tostore a lot of data (as is the case in FIG. 4 a and Nyquist sampling),and then discarding it, compressed sensing, as depicted in FIG. 4 b,saves valuable power in the way it performs compression. Compressedsensing is a relatively new phenomenon that uses knowledge of signalsparsity. The compressed sensing front end will require a randomizationmatrix to mix with the input signal. This will spread the frequencycontent of the signal and prevent eavesdropping, much like with spreadspectrum communication.

By utilizing compressed sensing analog-to-digital conversion block 150,the sensor network of FIGS. 2 and 3 will use substantially less powerthan the prior art sensor network of FIG. 1. The compressed sensinganalog-to-digital conversion block 150 relies upon an inherent sparsityof an input signal received from the amplifier 100, without priorknowledge of the input signal, for sampling of the input signal belowthe Nyquist rate to reduce power. The sampling rate of both thecompressed sensing analog-to-digital conversion block 150 andtransmitter 103 is reduced by the compression factor when compared toanalog-to-digital conversion block 101 and transmitter 103 of sensorblock 200. This leads to a direct savings in power. Post processor 102will also operate on less data in sensor block 210 than in sensor block200, leading to further power savings. With reference to FIG. 3,compressed sensing analog-to-digital conversion block 150 can utilize acompression rate of 8×-16×, which will lead to a similar decrease inpower consumption for sensor block 210 compared to prior art sensorblock 200.

Portions of the article “An Introduction to Compressive Sampling,”Emmanuel J. Candes and Michael B. Wakin, IEEE Signal ProcessingMagazine, March 2008, which is incorporated by reference herein, areexplicitly set forth below:

Conventional approaches to sampling signals or images follow Shannon'scelebrated theorem: the sampling rate must be at least twice the maximumfrequency present in the signal (the so-called Nyquist rate). In fact,this principle underlies nearly all signal acquisition protocols used inconsumer audio and visual electronics, medical imaging devices, radioreceivers, and so on. (For some signals, such as images that are notnaturally bandlimited, the sampling rate is dictated not by the Shannontheorem but by the desired temporal or spatial resolution. However, itis common in such systems to use an antialiasing low-pass filter to bandlimit the signal before sampling, and so the Shannon theorem plays animplicit role.) In the field of data conversion, for example, standardanalog-to-digital converter (ADC) technology implements the usualquantized Shannon representation: the signal is uniformly sampled at orabove the Nyquist rate. (IEEE Signal Processing Magazine March 2008,page 21).

This article surveys the theory of compressive sampling, also known ascompressed sensing or CS, is a novel sensing/sampling paradigm that goesagainst the common wisdom in data acquisition. CS theory asserts thatone can recover certain signals and images from far fewer samples ormeasurements than traditional methods use. To make this possible, CSrelies on two principles: sparsity, which pertains to the signals ofinterest, and incoherence, which pertains to the sensing modality.

-   -   Sparsity expresses the idea that the “information rate” of a        continuous time signal may be much smaller than suggested by its        bandwidth, or that a discrete-time signal depends on a number of        degrees of freedom which is comparably much smaller than its        (finite) length. More precisely, CS exploits the fact that many        natural signals are sparse or compressible in the sense that        they have concise representations when expressed in the proper        basis Ψ.    -   Incoherence extends the duality between time and frequency and        expresses the idea that objects having a sparse representation        in Ψ must be spread out in the domain in which they are        acquired, just as a Dirac or a spike in the time domain is        spread out in the frequency domain. Put differently, incoherence        says that unlike the signal of interest, the sampling/sensing        waveforms have an extremely dense representation in Ψ.

The crucial observation is that one can design efficient sensing orsampling protocols that capture the useful information content embeddedin a sparse signal and condense it into a small amount of data. Theseprotocols are nonadaptive and simply require correlating the signal witha small number of fixed waveforms that are incoherent with thesparsifying basis. What is most remarkable about these samplingprotocols is that they allow a sensor to very efficiently capture theinformation in a sparse signal without trying to comprehend that signal.Further, there is a way to use numerical optimization to reconstruct thefull-length signal from the small amount of collected data. In otherwords, CS is a very simple and efficient signal acquisition protocolwhich samples—in a signal independent fashion—at a low rate and lateruses computational power for reconstruction from what appears to be anincomplete set of measurements. (IEEE Signal Processing Magazine March2008, page 22, left column).

Incoherence and Sensing of Sparse Signals

This section presents the two fundamental premises underlying CS:sparsity and incoherence.

Sparsity

Many natural signals have concise representations when expressed in aconvenient basis. Consider, for example, the image in FIG. 1( a) and itswavelet transform in (b). Although nearly all the image pixels havenonzero values, the wavelet coefficients offer a concise summary: mostcoefficients are small and the relatively few large coefficients capturemost of the information . . . . In plain terms, one can “throw away” alarge fraction of the coefficients without much loss. FIG. 1( c) showsan example where the perceptual loss is hardly noticeable from amegapixel image to its approximation obtained by throwing away 97.5% ofthe coefficients.

This principle is, of course, what underlies most modern lossy coderssuch as JPEG-2000 [4] and many others, since a simple method for datacompression would be to compute x from f and then (adaptively) encodethe locations and values of the S significant coefficients. Such aprocess requires knowledge of all the n coefficients x, as the locationsof the significant pieces of information may not be known in advance(they are signal dependent); in our example, they tend to be clusteredaround edges in the image. More generally, sparsity is a fundamentalmodeling tool which permits efficient fundamental signal processing;e.g., accurate statistical estimation and classification, efficient datacompression, and so on. This article is about a more surprising andfar-reaching implication, however, which is that sparsity hassignificant bearings on the acquisition process itself. Sparsitydetermines how efficiently one can acquire signals nonadaptively. (IEEESignal Processing Magazine March 2008, page 23, left and right column).

Incoherent Sampling

. . . In plain English, the Coherence measures the largest correlationbetween any two elements of the sensing basis φ and the representationbasis Ψ; see also [5]. If φ and Ψ contain correlated elements, thecoherence is large. Otherwise, it is small . . . CS is mainly concernedwith low coherence pairs. (IEEE Signal Processing Magazine March 2008,page 23, right column).

We wish to make three comments:

-   -   1) The role of the coherence is completely transparent; the        smaller the coherence, the fewer samples are needed, hence our        emphasis on low coherence systems in the previous section.

2) One suffers no information loss by measuring just about any set of mcoefficients which may be far less than the signal size apparentlydemands. If μ(φ, Ψ) is equal or close to one, then on the order of S logn samples suffice instead of n.

3) The signal f can be exactly recovered from our condensed data set byminimizing a convex functional which does not assume any knowledge aboutthe number of nonzero coordinates of x, their locations, or theiramplitudes which we assume are all completely unknown a priori. We justrun the algorithm and if the signal happens to be sufficiently sparse,exact recovery occurs.

The theorem indeed suggests a very concrete acquisition protocol: samplenonadaptively in an incoherent domain and invoke linear programmingafter the acquisition step. Following this protocol would essentiallyacquire the signal in a compressed form. All that is needed is a decoderto “decompress” this data; this is the role of 11 minimization. (IEEESignal Processing Magazine March 2008, page 24, right column).

This example shows that a number of samples just about 4× the sparsitylevel suffices. Many researchers have reported on similar empiricalsuccesses. There is de facto a known four-to-one practical rule whichsays that for exact recovery, one needs about four incoherent samplesper unknown nonzero term. (IEEE Signal Processing Magazine March 2008,page 26, left column).

What is Compressive Sampling?

Data acquisition typically works as follows: massive amounts of data arecollected only to be—in large part—discarded at the compression stage tofacilitate storage and transmission. In the language of this article,one acquires a high-resolution pixel array f, computes the complete setof transform coefficients, encode the largest coefficients and discardall the others, essentially ending up with fS. This process of massivedata acquisition followed by compression is extremely wasteful (one canthink about a digital camera which has millions of imaging sensors, thepixels, but eventually encodes the picture in just a few hundredkilobytes).

CS operates very differently, and performs as “if it were possible todirectly acquire just the important information about the object ofinterest.” By taking about O(S log(n/S)) random projections as in“Random Sensing,” one has enough information to reconstruct the signalwith accuracy at least as good as that provided by fS, the best S-termapproximation—the best compressed representation—of the object. In otherwords, CS measurement protocols essentially translate analog data intoan already compressed digital form so that one can—at least inprinciple—obtain super-resolved signals from just a few sensors. Allthat is needed after the acquisition step is to “decompress” themeasured data. (IEEE Signal Processing Magazine March 2008, page 28,left column).

Applications

The fact that a compressible signal can be captured efficiently using anumber of incoherent measurements that is proportional to itsinformation level S<<n has implications that are far reaching andconcern a number of possible applications. (IEEE Signal ProcessingMagazine March 2008, page 28, right column).

The point here is that even though the amount of data is ridiculouslysmall, one has nevertheless captured most of the information containedin the signal. This, in a nutshell, is why CS holds such great promise.(IEEE Signal Processing Magazine March 2008, page 30, left column).

In another embodiment, to further reduce power, post processor 102 ortransmitter 103 can queue the packets of compressed data in memory andthen transmit in burst mode instead of in a continual fashion.

The proposed network helps in managing “big data.” Big data consists of3 components: velocity, volume and value. In the embodiment of FIG. 2,computing device 300 is connected to cloud 400 where the data isextracted in the raw form for processing. In this embodiment, the datais processed by cloud computing device 400 and not computing device 300.Because computing device 300 does not perform the data extraction andprocessing, it too can be optimized for low power consumption. Usingcompressed sensing will lower the storage cost for the data by as muchas 10×-20×.

FIGS. 5 and 6 show an application of the invention to body areanetworks. The data is transmitted from sensor block 210, sensor block211, sensor block 212, and any number of other sensor blocks 21 n, usingthe same functional design of sensor block 210 shown in FIGS. 2 and 3.Here, sensor block 210 comprises EEG (electroencephalography) electrodes310 for obtaining electrical data from a human patient's scalp, sensorblock 211 comprises ECG (electrocardiography) electrodes 311 forobtaining electrical data from a human patient's heart, and sensor block212 comprises CTG (cardiotocography) electrodes 312 for obtainingelectrical data from human patient's uterine contractions and fetalheartbeat. Sensor block 21 n comprises electrodes 31 n, or otherphysical sensors, for obtaining other medical or biological data from ahuman patient.

Computing device 300 here is a smart phone. Computing device 300optionally comprises a software application that enables a user ofcomputing device 300 to view graphical or numerical representations ofthe data collected by sensor block 210, sensor block 211, and othersensor blocks 21 n. Concurrently, the data will be transmitted to cloudcomputing device 400 where it can be processed.

References to the present invention herein are not intended to limit thescope of any claim or claim term, but instead merely make reference toone or more features that may be covered by one or more of the claims.Materials, processes and numerical examples described above areexemplary only, and should not be deemed to limit the claims. It shouldbe noted that, as used herein, the terms “over” and “on” bothinclusively include “directly on” (no intermediate materials, elementsor space disposed there between) and “indirectly on” (intermediatematerials, elements or space disposed there between). Likewise, the term“adjacent” includes “directly adjacent” (no intermediate materials,elements or space disposed there between) and “indirectly adjacent”(intermediate materials, elements or space disposed there between). Forexample, forming an element “over a substrate” can include forming theelement directly on the substrate with no intermediatematerials/elements there between, as well as forming the elementindirectly on the substrate with one or more intermediatematerials/elements there between.

What is claimed is:
 1. A sensor block for collecting data from a humanpatient, comprising: an amplifier; a compressed sensinganalog-to-digital conversion block coupled to the amplifier; thecompressed sensing analog-to-digital conversion block relying upon aninherent sparsity and incoherence of an input signal received from theamplifier, without prior knowledge of the input signal, for sampling ofthe input signal below the Nyquist rate to reduce power; a postprocessor coupled to the compressed sensing analog-to-digital conversionblock; a transmitter coupled to the post processor; and an antenna. 2.The sensor block of claim 1, wherein the compressed sensinganalog-to-digital conversion block comprises a random number generatorfor encrypting data.
 3. The sensor block of claim 1, wherein thecompressed sensing analog-to-digital conversion block utilizes acompression factor of at least
 8. 4. The sensor block of claim 1,wherein the sensor block further comprises electroencephalographyelectrodes.
 5. The sensor block of claim 1, wherein the sensor blockfurther comprises electrocardiography electrodes.
 6. The sensor block ofclaim 1, wherein the sensor block further comprises cardiotocographyelectrodes.
 7. A body area network for collecting and processing datafrom a human patient, comprising: a sensor block comprising: anamplifier; a compressed sensing analog-to-digital conversion blockcoupled to the amplifier, the compressed sensing analog-to-digitalconversion block relying upon an inherent sparsity and incoherence of aninput signal received from the amplifier, without prior knowledge of theinput signal, for sampling of the input signal below the Nyquist rate toreduce power; a post processor coupled to the compressed sensinganalog-to-digital conversion block; a transmitter coupled to the postprocessor; and an antenna; a smartphone coupled to the sensor block overa wireless network; and a cloud computing device coupled to thesmartphone over another wireless network.
 8. The body area network ofclaim 7, wherein the compressed sensing analog-to-digital conversionblock comprises a random number generator for encrypting data.
 9. Thebody area network of claim 7, wherein the compressed sensinganalog-to-digital conversion block utilizes a compression factor of atleast
 8. 10. The body area network of claim 7, wherein the sensor blockfurther comprises electroencephalography sensors.
 11. The body areanetwork of claim 7, wherein the sensor block further compriseselectrocardiography sensors.
 12. The body area network of claim 7,wherein the sensor block further comprises cardiotocography sensors. 13.A method of collecting and processing data from a human patient, themethod comprising the steps of: obtaining data from the human patientusing electrodes; generating compressed data using a compressed sensinganalog-to-digital conversion block operating on the data from the humanpatient, the compressed sensing analog-to-digital conversion blockrelying upon an inherent sparsity and incoherence of an input signalreceived from the amplifier, without prior knowledge of the inputsignal, for sampling of the input signal below the Nyquist rate toreduce power; processing the compressed data using a post processor togenerate processed data; transmitting the processed data to a smartphoneover a wireless network; and transmitting the processing data from thesmartphone to a cloud computing device over another wireless network.14. The method of claim 13, wherein the data from the human patientcomprises electroencephalography data.
 15. The method of claim 13,wherein the data from the human patient comprises electrocardiographydata.
 16. The method of claim 13, wherein the data from the humanpatient comprises cardiotocography data.
 17. The method of claim 13,further comprising the step of: generating graphical representations ofthe processed data on the smartphone.
 18. The method of claim 17,further comprising the step of: generating numerical representations ofthe processed data on the smartphone.
 19. The method of claim 13,further comprising the step of: queuing packets of processed data in amemory.
 20. The method of claim 19, wherein the step of transmitting theprocessed data to a smartphone comprises transmitting packets ofprocessed data in a burst mode.